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This product shall be used in conjunction with the Endoscopic Video Image Processor and other peripheral equipment for observation, diagnosis, photography and treatment within the bladder, urethra, and kidney.

The device is suitable for professional healthcare facility environments such as hospitals and clinics.

This product shall not be used for other purposes.

The Single-Use Video Flexible Cysto-Nephroscope mainly consist of insertion portion, handle and connector section which include a detachable video cable which connect the endoscope to the Endoscopic Video Image Processor.

The provided FDA 510(k) clearance letter and summary describe a physical medical device (Single-Use Video Flexible Cysto-Nephroscope), not an AI/ML-driven software device. Therefore, the information requested about acceptance criteria and studies (especially those related to AI performance, ground truth, expert adjudication, MRMC studies, or standalone algorithm performance) is not applicable to this document.

The document discusses the substantial equivalence of the new device to a predicate device based on non-clinical performance testing of the physical properties and functionality of the endoscope itself. There is no mention of any AI component or software that processes images or provides diagnostic assistance.

Here's a breakdown of what is available in the document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document broadly states that "The proposed device has met the testing acceptance criteria in accordance with internal requirements and applicable standards to support substantial equivalence of the proposed device." However, it does not provide a specific table listing detailed acceptance criteria and the corresponding reported performance values for each physical test. It only lists the types of tests performed.

• Sterilization Validation:
  • Acceptance Criteria: Determined by ISO 11135:2014 half-cycle method to establish routine control and monitoring parameters.
  • Reported Performance: Method validated. (Specific parameters not provided).
• Shelf Life and Simulated Transportation Distribution followed by Sterile Packaging Integrity Test:
  • Acceptance Criteria: Validated according to ASTM F1980-21, ISO11607-1:2019, ISO11607-2:2019, ASTM F1929-23, ASTM F88/F88M-23, ASTM F1886/F1886M-16, ASTM D4169-23.
  • Reported Performance: Validated. (Specific results not provided).
• Performance Testing (using methods and acceptance criteria from K241500):
  • Surface and Edges:
    • Acceptance Criteria: Not specified but refers to previous submission K241500.
    • Reported Performance: Met testing acceptance criteria.
  • Deflection system and Fatigue test of Rocker and Bending Section: (Note: Downward deflection angle changed from 135° to 225° for all models).
    • Acceptance Criteria: Not specified but refers to previous submission K241500.
    • Reported Performance: Met testing acceptance criteria.

Information Not Applicable to this Device/Document (for AI/ML products):

The following points are typically relevant for AI/ML device submissions but are not found in this document because it pertains to a physical, non-AI medical instrument.

• Sample sized used for the test set and the data provenance: Not applicable. Evaluation was based on physical device testing, not a dataset.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. No ground truth for an AI algorithm.
• Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
• If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance: Not applicable.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable.
• The sample size for the training set: Not applicable.
• How the ground truth for the training set was established: Not applicable.

Summary of Study Type:

The study described is a non-clinical performance testing study for a physical medical device (endoscope), designed to demonstrate substantial equivalence to a previously cleared predicate device. It involves component and system-level testing of physical characteristics, sterilization, packaging integrity, and functionality based on established industry standards and previous submission's methodologies. There is no AI or software performance evaluation in this clearance.

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Endoscopic Video Image Processor

This device is used in conjunction with the video endoscopes produced by our company to process the images collected by the video endoscopes and send them to the display, and provide power for the endoscope.

Single-Use Video Flexible Cysto-Nephroscope

This product shall be used in conjunction with the Endoscopic Video Image Processor and other peripheral equipment for observation, diagnosis, photography and treatment within the bladder, urethra, and kidney. The device is suitable for professional healthcare facility environments such as hospitals and clinics.

This product shall not be used for other purposes.

The proposed device, Cysto-Nephroscope System, which includes a Single-Use Video Flexible Cysto-Nephroscope and an Endoscopic Video Image Processor is intended for observation, diagnosis, photography and treatment within the bladder, urethra, and kidney.

The Single-Use Video Flexible Cysto-Nephroscope is available in two models with the different position of "U" and "D" mark on the handle and different deflection, RP-U-C01F, RP-U-C01F5. The Endoscopic Video Image Processor has only one model, RP-IPD-V2000EF.

The Single-Use Video Flexible Cysto-Nephroscope is sterile and single-patient-use device. The Endoscopic Video Image Processor is nonsterile and multi-patient-use device, and it will be cleaned and disinfected before first use and after each use.

The provided text describes a 510(k) premarket notification for an Endoscopic Video Image Processor and Single-Use Video Flexible Cysto-Nephroscope. The document focuses on demonstrating substantial equivalence to a predicate device through performance testing. However, it does not contain specific acceptance criteria, numerical performance metrics for the device against these criteria, or a detailed study description with sample sizes, ground truth establishment, or expert involvement as requested.

Therefore, I cannot provide the detailed table and study information you've requested from the provided text. The document states that the system "complies with the standard requirements" of ISO 8600 and IEC 62471:2006, and that optical and mechanical performance is "similar as those of the predicate device," but it does not quantify these compliance levels or similarities.

Below is what can be extracted or inferred based on the provided text, highlighting the absence of the specific data you requested.

Absence of Detailed Acceptance Criteria and Device Performance:

The document broadly states that the device was evaluated for performance but does not provide a table specifying predefined acceptance criteria (e.g., minimum resolution, specific signal-to-noise ratio values) and the corresponding reported device performance values. It generally concludes that the device "complies with the standard requirements" (e.g., ISO 8600, IEC 62471:2006) and that its performance is "similar as those of the predicate device," but no quantitative data to support these statements is present.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

The document does not specify the sample sizes used for any of the performance tests (e.g., number of instruments tested, number of images/frames evaluated). The data provenance is not explicitly stated beyond the company being Guangzhou Red Pine Medical Instrument Co., Ltd. from China. It is not specified if the tests involved retrospective or prospective data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

The document does not mention the use of experts to establish ground truth for the test set, nor does it specify their number or qualifications. The performance evaluation seems to be based on instrumental measurements and comparisons to a predicate device, rather than expert interpretation of images.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

No adjudication method is mentioned, as there is no indication of expert review or consensus building for ground truth.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

A multi-reader multi-case (MRMC) comparative effectiveness study was not mentioned. The document primarily focuses on technical performance characteristics of the device itself and comparison to a predicate device, not on human reader performance with or without AI assistance. The device described (Endoscopic Video Image Processor and Cysto-Nephroscope) is an imaging system, not an AI-driven diagnostic aid that assists human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

This point is not applicable as the device is an imaging system and not an AI algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The document does not describe the establishment of a "ground truth" in the diagnostic sense (e.g., expert consensus for disease presence or absence, pathological confirmation). Instead, the "ground truth" for these engineering and performance tests would largely be the established specifications within the relevant ISO and IEC standards, and the performance characteristics of the predicate device for comparative testing.

8. The sample size for the training set

This is not applicable, as the document describes an endoscopic imaging system, not an AI algorithm that requires a training set.

9. How the ground truth for the training set was established

This is not applicable, as there is no training set for an AI algorithm.

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